identifying defective substances from existing software frameworks is an issue of extraordinary significance for expanding both software quality and the proficiency of software testing related exercises. We present in this paper a novel methodology for anticipating software defects utilizing fuzzy decision trees. Through the fuzzy methodology we plan to all the more likely adapt to clamor and loose data. A fuzzy decision tree will be prepared to recognize whether a software module is defective or not. Two open source software frameworks are utilized for tentatively assessing our methodology. The acquired outcomes feature that the fuzzy decision tree approach beats the non-fuzzy one on practically all contextual investigations utilized for assessment. Contrasted with the methodologies utilized in the writing, the fuzzy decision tree classifier is appeared to be more effective than the greater part of the other machine learning-based classifiers.

Software defect prediction,Machine learning,Decision tree, Fuzzy theory

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